On Monday, April 16, Kim Silverman, principal research scientist at Apple, gave a talk at ICSI on speech synthesis, giving an overview of text normalization, named entity extraction, part-of-speech tagging, phrasing, topic tracking, pronunciation, intonation, duration, phonology, phonetics, and signal representation. Read the full abstract and bio at https://www.icsi.berkeley.edu/icsi/events/2012/04/kim-silverman-talk .
Title: "Speech Synthesis"
Abstract:
Conversion of text to speech requires processing at many levels of representation. This presentation will systematically step through text normalization, named entity extraction, part-of-speech tagging, phrasing, topic tracking, pronunciation, intonation, duration, phonology, phonetics, and signal representation. Examples of each stage, the difficulties encountered, and some typical approaches will be illustrated. This will provide a solid background for students to evaluate recent investigative approaches to HMM synthesis, and to modeling of speaker emotion.
Bio:
Kim Silverman is a principal research scientist at Apple, where among other things he led the development of the Alex text-to-speech synthesis system that is the flagship American English voice in OS X. He is first author on the ToBI standard for transcribing speech prosody.
Read the full bio at https://www.icsi.berkeley.edu/icsi/events/2012/04/kim-silverman-talk.

published:07 Aug 2013

views:14592

Heiga Zen, GoogleAbstract: Recent progress in generative modeling has improved the naturalness of synthesized speech significantly. In this talk I will summarize these generative model-based approaches for speech synthesis and describe possible future directions.

[VOLUME WARNING] This is what happens when you throw raw audio (which happens to be a cute voice) into a neural network and then tell it to spit out what it's learned.
This is a recurrent neural network (LSTM type) with 3 layers of 680 neurons each, trying to find patterns in audio and reproduce them as well as it can. It's not a particularly big network considering the complexity and size of the data, mostly due to computing constraints, which makes me even more impressed with what it managed to do.
The audio that the network was learning from is voice actress Kanematsu Yuka voicing Hinata from Pure Pure. I used 11025 Hz, 8-bit audio because sound files get big quickly, at least compared to text files - 10 minutes already runs to 6.29MB, while that much plain text would take weeks or months for a human to read.
UPDATE: By popular demand, I have uploaded a video where I did this with male English voice, too: https://www.youtube.com/watch?v=NG-LATBZNBs
I was using the program "torch-rnn" (https://github.com/jcjohnson/torch-rnn/), which is actually designed to learn from and generate plain text. I wrote a program that converts any data into UTF-8 text and vice-versa, and to my excitement, torch-rnn happily processed that text as if there was nothing unusual. I did this because I don't know where to begin coding my own neural network program, but this workaround has some annoying restraints. E.g. torch-rnn doesn't like to output more than about 300KB of data, hence all generated sounds being only ~27 seconds long.
It took roughly 29 hours to train the network to ~35 epochs (74,000 iterations) and over 12 hours to generate the samples (output audio). These times are quite approximate as the same server was both training and sampling (from past network "checkpoints") at the same time, which slowed it down. Huge thanks go to Melan for letting me use his server for this fun project! Let's try a bigger network next time, if you can stand waiting an hour for 27 seconds of potentially-useless audio. xD
I feel that my target audience couldn't possibly get any smaller than it is right now...EDIT: I have put some graphs of the training and validation losses on my blog for those who have asked what the losses were!
http://robbi-985.homeip.net/blog/?p=1760#settings
EDIT 2: I have been asked several times about my binary-to-UTF-8 program. The program basically substitutes any raw byte value for a valid UTF-8 encoding of a character. So after conversion, there'll be a maximum of 256 unique UTF-8 characters. I threw the program together in VB6, so it will only run on Windows. However, I rewrote all the important code in a C++-like pseudocode:
http://robbi-985.homeip.net/information/bintoutf8_pseudo.txt
Also, here is an English explanation of how my binary-to-UTF-8 program works:
http://robbi-985.homeip.net/information/bintoutf8_info.txt
EDIT 3: I have released my BinToUTF8 program to the public! Please have a look here:
http://robbi-985.homeip.net/blog/?p=1845

published:24 May 2016

views:351789

By popular demand, I threw my own voice into a neural network (3 times) and got it to recreate what it had learned along the way!
This is 3 different recurrent neural networks (LSTM type) trying to find patterns in raw audio and reproduce them as well as they can. The networks are quite small considering the complexity of the data. I recorded 3 different vocal sessions as training data for the network, trying to get more impressive results out of the network each time. The audio is 8-bit and a low sample rate because sound files get very big very quickly, making the training of the network take a very long time. Well over 300 hours of training in total went into the experiments with my voice that led to this video.
The graphs are created from log files made during training, and show the progress that it was making leading up to immediately before the audio that you hear at every point in the video. Their scrolling speeds up at points where I only show a short sample of the sound, because I wanted to dedicated more time to the more impressive parts. I included a lot of information in the video itself where it's relevant (and at the end), especially details about each of the 3 neural networks at the beginning of each of the 3 sections, so please be sure to check that if you'd like more details.
I'm less happy with the results this time around than in my last RNN+voice video (https://www.youtube.com/watch?v=FsVSZpoUdSU), because I've experimented much less with my own voice than I have with higher-pitched voices from various games and haven't found the ideal combination of settings yet. That's because I don't really want to hear the sound of my own voice, but so many people commented on my old video that they wanted to hear a neural network trained on a male English voice, so here we are now! Also, learning from a low-pitched voice is not as easy as with a high-pitched voice, for reasons explained in the first part of the video (basically, the most fundamental patterns are longer with a low-pitched voice).
The neural network software is the open-source "torch-rnn" (https://github.com/jcjohnson/torch-rnn/), although that is only designed to learn from plain text. Frankly, I'm still amazed at what a good job it does of learning from raw audio, with many overlapping patterns over longer timeframes than text. I made a program(*) that substitutes raw bytes in any file (e.g. audio) for valid UTF-8 text characters and torch-rnn happily learned from it. My program also substituted torch-rnn's generated text back into raw bytes to get audio again. I do not understand the mathematics and low-level algorithms that go make a neural network work, and I cannot program my own, so please check the code and .md files at torch-rnn's Github page for details. Also, torch-rnn is actually a more-efficient fork of an earlier software called char-rnn (https://github.com/karpathy/char-rnn), whose project page also has a lot of useful information.
I will probably soon release the program that I wrote to create the line graphs from CSV files. It can make images up to 16383 pixels wide/tall with customisable colours, from CSV files with hundreds of thousands of lines, in a few seconds. All free software I could find failed hideously at this (e.g. OpenOffice Calc took over a minute to refresh the screen with only a fraction of that many lines, during which time it stopped responding; the lines overlapped in an ugly way that meant you couldn't even see the average value; and "exporting" graphs is limited to pressing Print Screen, so you're limited to the width of your screen... really?).
(*)Here is the code rewritten from VB6 in a C++-like pseudocode:
http://robbi-985.homeip.net/information/bintoutf8_pseudo.txt
Also, here is an English explanation of the idea behind how it works:
http://robbi-985.homeip.net/information/bintoutf8_info.txt
EDIT: I have released my BinToUTF8 program to the public! Please have a look here:
http://robbi-985.homeip.net/blog/?p=1845

published:24 Dec 2016

views:801904

Julie's voice is so realistic,,,,
Thank you! for watching this video please leave a like if you enjoyed the video & Subscribe for more videos.
●DOWNLOAD Voice Packs Here ►https://goo.gl/gTvlqG
FOLLOW "kilObit" ON SOCIAL NETWORKS
FACEBOOK ►https://www.facebook.com/kil0bit
TWITTER ►https://twitter.com/kil0bit
WEBSITE ►http://goo.gl/qwCTD9
Visit the official Blog/Website of "kilObit' get everything in one place & the website looks cool, All the downloading things goes on my site so be sure to check out maybe you will find something helpful or entertaining there.
ADMINS SOCIAL NETWORK LINKS
▪FACEBOOK ►https://goo.gl/ebfmBo
▪TWITTER ►https://goo.gl/JkMX0p
▪INSTAGRAM ►https://goo.gl/SNmyt1
This is not a important part of the description but this is the person behind the "kilObit" you may wanna know him.
Thank You! for all of your support...

published:28 Dec 2016

views:212286

What is SPEECH SYNTHESIS? What does SPEECH SYNTHESIS mean? SPEECH SYNTHESIS meaning - SPEECH SYNTHESIS definition - SPEECH SYNTHESIS explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ
Speech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech computer or speech synthesizer, and can be implemented in software or hardware products. A text-to-speech (TTS) system converts normal language text into speech; other systems render symbolic linguistic representations like phonetic transcriptions into speech.
Synthesized speech can be created by concatenating pieces of recorded speech that are stored in a database. Systems differ in the size of the stored speech units; a system that stores phones or diphones provides the largest output range, but may lack clarity. For specific usage domains, the storage of entire words or sentences allows for high-quality output. Alternatively, a synthesizer can incorporate a model of the vocal tract and other human voice characteristics to create a completely "synthetic" voice output.
The quality of a speech synthesizer is judged by its similarity to the human voice and by its ability to be understood clearly. An intelligible text-to-speech program allows people with visual impairments or reading disabilities to listen to written words on a home computer. Many computer operating systems have included speech synthesizers since the early 1990s.
A text-to-speech system (or "engine") is composed of two parts: a front-end and a back-end. The front-end has two major tasks. First, it converts raw text containing symbols like numbers and abbreviations into the equivalent of written-out words. This process is often called text normalization, pre-processing, or tokenization. The front-end then assigns phonetic transcriptions to each word, and divides and marks the text into prosodic units, like phrases, clauses, and sentences. The process of assigning phonetic transcriptions to words is called text-to-phoneme or grapheme-to-phoneme conversion. Phonetic transcriptions and prosody information together make up the symbolic linguistic representation that is output by the front-end. The back-end—often referred to as the synthesizer—then converts the symbolic linguistic representation into sound. In certain systems, this part includes the computation of the target prosody (pitch contour, phoneme durations), which is then imposed on the output speech.

published:20 Oct 2017

views:159

Welcome to this talking presentation! You are wondering what is the process to create a synthetic voice? And how does it work? Listen to this short story featuring some of our bright voices. This talking presentation uses SlideTalk online solution and Acapela Group voices.
More on http://www.slidetalk.net and http:www.acapela-group.com

published:28 Feb 2014

views:38470

Today's artificial speech tends to sound robotic, but using a new system called WaveNet, Google Deepmind has created a new system that produces much more natural human speech. While not perfect, it is 50% better than current technologies. Since it is at core a general audio processor, it can also create music.
Find out more at: http://www.33rdsquare.com/2016/09/deepmind-uses-deep-neural-networks-to.html

published:12 Sep 2016

views:33303

Synthesizing Obama: Learning Lip Sync from Audio
Supasorn Suwajanakorn, Steven M. Seitz, Ira Kemelmacher-Shlizerman
SIGGRAPH 2017
Given audio of President Barack Obama, we synthesize a high quality video of him speaking with accurate lip sync, composited into a target video clip. Trained on many hours of his weekly address footage, a recurrent neural network learns the mapping from raw audio features to mouth shapes. Given the mouth shape at each time instant, we synthesize high quality mouth texture, and composite it with proper 3D pose matching to change what he appears to be saying in a target video to match the input audio track
http://grail.cs.washington.edu/projects/AudioToObama/

Synthesized speech can be created by concatenating pieces of recorded speech that are stored in a database. Systems differ in the size of the stored speech units; a system that stores phones or diphones provides the largest output range, but may lack clarity. For specific usage domains, the storage of entire words or sentences allows for high-quality output. Alternatively, a synthesizer can incorporate a model of the vocal tract and other human voice characteristics to create a completely "synthetic" voice output.

The quality of a speech synthesizer is judged by its similarity to the human voice and by its ability to be understood clearly. An intelligible text-to-speech program allows people with visual impairments or reading disabilities to listen to written works on a home computer. Many computer operating systems have included speech synthesizers since the early 1990s.

Speech

Speech is the vocalized form of humancommunication. It is based upon the syntactic combination of lexicals and names that are drawn from very large (usually about 1,000 different words) vocabularies. Each spoken word is created out of the phonetic combination of a limited set of vowel and consonant speech sound units. These vocabularies, the syntax which structures them, and their set of speech sound units differ, creating the existence of many thousands of different types of mutually unintelligible human languages. Most human speakers are able to communicate in two or more of them, hence being polyglots. The vocal abilities that enable humans to produce speech also provide humans with the ability to sing.

A gestural form of human communication exists for the deaf in the form of sign language. Speech in some cultures has become the basis of a written language, often one that differs in its vocabulary, syntax and phonetics from its associated spoken one, a situation called diglossia. Speech in addition to its use in communication, it is suggested by some psychologists such as Vygotsky is internally used by mental processes to enhance and organize cognition in the form of an interior monologue.

For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated. This determines which character was read.

"Speech Synthesis," Kim Silverman

On Monday, April 16, Kim Silverman, principal research scientist at Apple, gave a talk at ICSI on speech synthesis, giving an overview of text normalization, named entity extraction, part-of-speech tagging, phrasing, topic tracking, pronunciation, intonation, duration, phonology, phonetics, and signal representation. Read the full abstract and bio at https://www.icsi.berkeley.edu/icsi/events/2012/04/kim-silverman-talk .
Title: "Speech Synthesis"
Abstract:
Conversion of text to speech requires processing at many levels of representation. This presentation will systematically step through text normalization, named entity extraction, part-of-speech tagging, phrasing, topic tracking, pronunciation, intonation, duration, phonology, phonetics, and signal representation. Examples of each stage, the difficulties encountered, and some typical approaches will be illustrated. This will provide a solid background for students to evaluate recent investigative approaches to HMM synthesis, and to modeling of speaker emotion.
Bio:
Kim Silverman is a principal research scientist at Apple, where among other things he led the development of the Alex text-to-speech synthesis system that is the flagship American English voice in OS X. He is first author on the ToBI standard for transcribing speech prosody.
Read the full bio at https://www.icsi.berkeley.edu/icsi/events/2012/04/kim-silverman-talk.

38:21

Generative Model-Based Text-to-Speech Synthesis

Generative Model-Based Text-to-Speech Synthesis

Generative Model-Based Text-to-Speech Synthesis

Heiga Zen, GoogleAbstract: Recent progress in generative modeling has improved the naturalness of synthesized speech significantly. In this talk I will summarize these generative model-based approaches for speech synthesis and describe possible future directions.

Neural Network Learns to Generate Voice (RNN/LSTM)

[VOLUME WARNING] This is what happens when you throw raw audio (which happens to be a cute voice) into a neural network and then tell it to spit out what it's learned.
This is a recurrent neural network (LSTM type) with 3 layers of 680 neurons each, trying to find patterns in audio and reproduce them as well as it can. It's not a particularly big network considering the complexity and size of the data, mostly due to computing constraints, which makes me even more impressed with what it managed to do.
The audio that the network was learning from is voice actress Kanematsu Yuka voicing Hinata from Pure Pure. I used 11025 Hz, 8-bit audio because sound files get big quickly, at least compared to text files - 10 minutes already runs to 6.29MB, while that much plain text would take weeks or months for a human to read.
UPDATE: By popular demand, I have uploaded a video where I did this with male English voice, too: https://www.youtube.com/watch?v=NG-LATBZNBs
I was using the program "torch-rnn" (https://github.com/jcjohnson/torch-rnn/), which is actually designed to learn from and generate plain text. I wrote a program that converts any data into UTF-8 text and vice-versa, and to my excitement, torch-rnn happily processed that text as if there was nothing unusual. I did this because I don't know where to begin coding my own neural network program, but this workaround has some annoying restraints. E.g. torch-rnn doesn't like to output more than about 300KB of data, hence all generated sounds being only ~27 seconds long.
It took roughly 29 hours to train the network to ~35 epochs (74,000 iterations) and over 12 hours to generate the samples (output audio). These times are quite approximate as the same server was both training and sampling (from past network "checkpoints") at the same time, which slowed it down. Huge thanks go to Melan for letting me use his server for this fun project! Let's try a bigger network next time, if you can stand waiting an hour for 27 seconds of potentially-useless audio. xD
I feel that my target audience couldn't possibly get any smaller than it is right now...EDIT: I have put some graphs of the training and validation losses on my blog for those who have asked what the losses were!
http://robbi-985.homeip.net/blog/?p=1760#settings
EDIT 2: I have been asked several times about my binary-to-UTF-8 program. The program basically substitutes any raw byte value for a valid UTF-8 encoding of a character. So after conversion, there'll be a maximum of 256 unique UTF-8 characters. I threw the program together in VB6, so it will only run on Windows. However, I rewrote all the important code in a C++-like pseudocode:
http://robbi-985.homeip.net/information/bintoutf8_pseudo.txt
Also, here is an English explanation of how my binary-to-UTF-8 program works:
http://robbi-985.homeip.net/information/bintoutf8_info.txt
EDIT 3: I have released my BinToUTF8 program to the public! Please have a look here:
http://robbi-985.homeip.net/blog/?p=1845

13:41

Neural Network Tries to Generate English Speech (RNN/LSTM)

Neural Network Tries to Generate English Speech (RNN/LSTM)

Neural Network Tries to Generate English Speech (RNN/LSTM)

By popular demand, I threw my own voice into a neural network (3 times) and got it to recreate what it had learned along the way!
This is 3 different recurrent neural networks (LSTM type) trying to find patterns in raw audio and reproduce them as well as they can. The networks are quite small considering the complexity of the data. I recorded 3 different vocal sessions as training data for the network, trying to get more impressive results out of the network each time. The audio is 8-bit and a low sample rate because sound files get very big very quickly, making the training of the network take a very long time. Well over 300 hours of training in total went into the experiments with my voice that led to this video.
The graphs are created from log files made during training, and show the progress that it was making leading up to immediately before the audio that you hear at every point in the video. Their scrolling speeds up at points where I only show a short sample of the sound, because I wanted to dedicated more time to the more impressive parts. I included a lot of information in the video itself where it's relevant (and at the end), especially details about each of the 3 neural networks at the beginning of each of the 3 sections, so please be sure to check that if you'd like more details.
I'm less happy with the results this time around than in my last RNN+voice video (https://www.youtube.com/watch?v=FsVSZpoUdSU), because I've experimented much less with my own voice than I have with higher-pitched voices from various games and haven't found the ideal combination of settings yet. That's because I don't really want to hear the sound of my own voice, but so many people commented on my old video that they wanted to hear a neural network trained on a male English voice, so here we are now! Also, learning from a low-pitched voice is not as easy as with a high-pitched voice, for reasons explained in the first part of the video (basically, the most fundamental patterns are longer with a low-pitched voice).
The neural network software is the open-source "torch-rnn" (https://github.com/jcjohnson/torch-rnn/), although that is only designed to learn from plain text. Frankly, I'm still amazed at what a good job it does of learning from raw audio, with many overlapping patterns over longer timeframes than text. I made a program(*) that substitutes raw bytes in any file (e.g. audio) for valid UTF-8 text characters and torch-rnn happily learned from it. My program also substituted torch-rnn's generated text back into raw bytes to get audio again. I do not understand the mathematics and low-level algorithms that go make a neural network work, and I cannot program my own, so please check the code and .md files at torch-rnn's Github page for details. Also, torch-rnn is actually a more-efficient fork of an earlier software called char-rnn (https://github.com/karpathy/char-rnn), whose project page also has a lot of useful information.
I will probably soon release the program that I wrote to create the line graphs from CSV files. It can make images up to 16383 pixels wide/tall with customisable colours, from CSV files with hundreds of thousands of lines, in a few seconds. All free software I could find failed hideously at this (e.g. OpenOffice Calc took over a minute to refresh the screen with only a fraction of that many lines, during which time it stopped responding; the lines overlapped in an ugly way that meant you couldn't even see the average value; and "exporting" graphs is limited to pressing Print Screen, so you're limited to the width of your screen... really?).
(*)Here is the code rewritten from VB6 in a C++-like pseudocode:
http://robbi-985.homeip.net/information/bintoutf8_pseudo.txt
Also, here is an English explanation of the idea behind how it works:
http://robbi-985.homeip.net/information/bintoutf8_info.txt
EDIT: I have released my BinToUTF8 program to the public! Please have a look here:
http://robbi-985.homeip.net/blog/?p=1845

5:18

BEST Text-To-Speech Voice (REAL HUMAN VOICE)

BEST Text-To-Speech Voice (REAL HUMAN VOICE)

BEST Text-To-Speech Voice (REAL HUMAN VOICE)

Julie's voice is so realistic,,,,
Thank you! for watching this video please leave a like if you enjoyed the video & Subscribe for more videos.
●DOWNLOAD Voice Packs Here ►https://goo.gl/gTvlqG
FOLLOW "kilObit" ON SOCIAL NETWORKS
FACEBOOK ►https://www.facebook.com/kil0bit
TWITTER ►https://twitter.com/kil0bit
WEBSITE ►http://goo.gl/qwCTD9
Visit the official Blog/Website of "kilObit' get everything in one place & the website looks cool, All the downloading things goes on my site so be sure to check out maybe you will find something helpful or entertaining there.
ADMINS SOCIAL NETWORK LINKS
▪FACEBOOK ►https://goo.gl/ebfmBo
▪TWITTER ►https://goo.gl/JkMX0p
▪INSTAGRAM ►https://goo.gl/SNmyt1
This is not a important part of the description but this is the person behind the "kilObit" you may wanna know him.
Thank You! for all of your support...

What is SPEECH SYNTHESIS? What does SPEECH SYNTHESIS mean? SPEECH SYNTHESIS meaning - SPEECH SYNTHESIS definition - SPEECH SYNTHESIS explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ
Speech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech computer or speech synthesizer, and can be implemented in software or hardware products. A text-to-speech (TTS) system converts normal language text into speech; other systems render symbolic linguistic representations like phonetic transcriptions into speech.
Synthesized speech can be created by concatenating pieces of recorded speech that are stored in a database. Systems differ in the size of the stored speech units; a system that stores phones or diphones provides the largest output range, but may lack clarity. For specific usage domains, the storage of entire words or sentences allows for high-quality output. Alternatively, a synthesizer can incorporate a model of the vocal tract and other human voice characteristics to create a completely "synthetic" voice output.
The quality of a speech synthesizer is judged by its similarity to the human voice and by its ability to be understood clearly. An intelligible text-to-speech program allows people with visual impairments or reading disabilities to listen to written words on a home computer. Many computer operating systems have included speech synthesizers since the early 1990s.
A text-to-speech system (or "engine") is composed of two parts: a front-end and a back-end. The front-end has two major tasks. First, it converts raw text containing symbols like numbers and abbreviations into the equivalent of written-out words. This process is often called text normalization, pre-processing, or tokenization. The front-end then assigns phonetic transcriptions to each word, and divides and marks the text into prosodic units, like phrases, clauses, and sentences. The process of assigning phonetic transcriptions to words is called text-to-phoneme or grapheme-to-phoneme conversion. Phonetic transcriptions and prosody information together make up the symbolic linguistic representation that is output by the front-end. The back-end—often referred to as the synthesizer—then converts the symbolic linguistic representation into sound. In certain systems, this part includes the computation of the target prosody (pitch contour, phoneme durations), which is then imposed on the output speech.

3:07

How does Text To Speech (TTS) work - by Acapela Voices

How does Text To Speech (TTS) work - by Acapela Voices

How does Text To Speech (TTS) work - by Acapela Voices

Welcome to this talking presentation! You are wondering what is the process to create a synthetic voice? And how does it work? Listen to this short story featuring some of our bright voices. This talking presentation uses SlideTalk online solution and Acapela Group voices.
More on http://www.slidetalk.net and http:www.acapela-group.com

3:33

DeepMind's WaveNet Text-To-Speech Algorithm

DeepMind's WaveNet Text-To-Speech Algorithm

DeepMind's WaveNet Text-To-Speech Algorithm

Today's artificial speech tends to sound robotic, but using a new system called WaveNet, Google Deepmind has created a new system that produces much more natural human speech. While not perfect, it is 50% better than current technologies. Since it is at core a general audio processor, it can also create music.
Find out more at: http://www.33rdsquare.com/2016/09/deepmind-uses-deep-neural-networks-to.html

8:01

Synthesizing Obama: Learning Lip Sync from Audio

Synthesizing Obama: Learning Lip Sync from Audio

Synthesizing Obama: Learning Lip Sync from Audio

Synthesizing Obama: Learning Lip Sync from Audio
Supasorn Suwajanakorn, Steven M. Seitz, Ira Kemelmacher-Shlizerman
SIGGRAPH 2017
Given audio of President Barack Obama, we synthesize a high quality video of him speaking with accurate lip sync, composited into a target video clip. Trained on many hours of his weekly address footage, a recurrent neural network learns the mapping from raw audio features to mouth shapes. Given the mouth shape at each time instant, we synthesize high quality mouth texture, and composite it with proper 3D pose matching to change what he appears to be saying in a target video to match the input audio track
http://grail.cs.washington.edu/projects/AudioToObama/

Arduino + Speakjet speech synthesis 'Nonsense Babbler'

What do you get if you cross an Arudino, a Speakjet IC and a bucket of verbal diarrhoea?
The first noise maker I've done based on a Speakjet. Proper text-to-speech is coming at some point down the line but I wanted to start with something odd.
Speakjet datasheet can be found here: https://www.sparkfun.com/datasheets/Components/General/speakjet-usermanual.pdf
The LEDs and reset button are done purely using built-in Speakjet features which are detailed in the datasheet.
The Arduino code I wrote can be found here: https://github.com/dannycarnage/SpeakjetNonsenseBabbler

8:51

C# SPEECH SYNTHESIS TUTORIAL

C# SPEECH SYNTHESIS TUTORIAL

C# SPEECH SYNTHESIS TUTORIAL

****** Inscreva-se: https://goo.gl/G4Ppnf ******
Descrição: In this Speech synthesis tutorial you're going to learn on how use the speech synthesis capability in your program in C#. To follow this tutorial you need to download and install the SpeechSDK5.1 in this link https://download.microsoft.com/download/B/4/3/B4314928-7B71-4336-9DE7-6FA4CF00B7B3/SpeechSDK51.exe after this you're good to go.
Patreon: https://goo.gl/A3iCR9
Twitter: https://twitter.com/pehlimaofficial
Python para iniciantes: https://www.youtube.com/playlist?list=PL39zyvnHdXh_osGdr5aMT5mj6cxNIiFlE
C# para iniciantes: https://www.youtube.com/playlist?list=PL39zyvnHdXh_iuHCk4bBKW-U5qOLfug8Z
F# para iniciantes: https://www.youtube.com/playlist?list=PL39zyvnHdXh8K9UgRWVmya_OIxyiPK8xA
FanPage: https://www.facebook.com/C%C3%B3digo-Logo-598277996996624/?ref=bookmarks
CódigoShare: https://www.facebook.com/groups/633347333472123/?ref=bookmarks

1:25:49

Building speech synthesis systems for Indian languages by Prof.Hema Murthy, IITM

Building speech synthesis systems for Indian languages by Prof.Hema Murthy, IITM

Building speech synthesis systems for Indian languages by Prof.Hema Murthy, IITM

3:23

How to make an Ai 1.Speech Synthesis

How to make an Ai 1.Speech Synthesis

How to make an Ai 1.Speech Synthesis

Hi folks this is a video about how to make an AI
If you have not seen the Ai Video then go and check it its awesome
Check out my website to get the full code of the program
http://destyy.com/wo3DRC
1 video each week

17:28

009 Microvox - Vintage Computer Speech Synthesis

009 Microvox - Vintage Computer Speech Synthesis

009 Microvox - Vintage Computer Speech Synthesis

In the latest episode of Coke and Strippers we bring a 1980's MicroVox voice synthesizer back to life using an Arduino Uno.
The MicroVox uses a 6502 process to look up words in a small ROM dictionary to send the correct phonemes to the SC-01a speech synthesis chip, or you can send just the phonemes yourself.
If you have projects you'd like to see built, post them in the comments below.
MusicFireworks by Jahzzar is licensed under a Attribution-ShareAlike 3.0InternationalLicense.
http://freemusicarchive.org/music/Jahzzar/Travellers_Guide/Fireworks
Music: Hello Friend... by The Birth and Death of Silence is licensed under a Attribution License.
http://freemusicarchive.org/music/The_Birth_and_Death_of_Silence/The_Birth_and_Death_of_Silence/The_Birth_and_Death_of_Silence_-_Promo_-_03_Hello_Friend
Music: Hello Friend... by The Birth and Death of Silence is licensed under a Attribution License.
Music:The Birth and Death of Silence by The Birth and Death of Silence is licensed under a Attribution License.
http://freemusicarchive.org/music/The_Birth_and_Death_of_Silence/The_Birth_and_Death_of_Silence/

1:36:17

HMM-based Speech Synthesis: Fundamentals and Its Recent Advances

HMM-based Speech Synthesis: Fundamentals and Its Recent Advances

HMM-based Speech Synthesis: Fundamentals and Its Recent Advances

The task of speech synthesis is to convert normal language text into speech. In recent years, hidden Markov model (HMM) has been successfully applied to acoustic modeling for speech synthesis, and HMM-based parametric speech synthesis has become a mainstream speech synthesis method. This method is able to synthesize highly intelligible and smooth speech sounds. Another significant advantage of this model-based parametric approach is that it makes speech synthesis far more flexible compared to the conventional unit selection and waveform concatenation approach. This talk will first introduce the overall HMM synthesis system architecture developed at USTC. Then, some key techniques will be described, including the vocoder, acoustic modeling, parameter generation algorithm, MSD-HMM for F0 modeling, context-dependent model training, etc. Our method will be compared with the unit selection approach and its flexibility in controlling voice characteristics will also be presented. The second part of this talk will describe some recent advances of HMM-based speech synthesis at the USTC speech group. The methods to be described include: 1) articulatory control of HMM-based speech synthesis, which further improves the flexibility of HMM-based speech synthesis by integrating phonetic knowledge, 2) LPS-GV and minimum KLD based parameter generation, which alleviates the over-smoothing of generated spectral features and improves the naturalness of synthetic speech, and 3) hybrid HMM-based/unit-selection approach which achieves excellent performance in the BlizzardChallenge speech synthesis evaluation events of recent years.

Generative Model-Based Text-to-Speech Synthesis

Heiga Zen, GoogleAbstract: Recent progress in generative modeling has improved the naturalness of synthesized speech significantly. In this talk I will summarize these generative model-based approaches for speech synthesis and describe possible future directions.

Prof. Simon King - Using Speech Synthesis to give Everyone their own Voice

ProfessorSimon King presents his Inaugural Lecture entitled, "Using speech synthesis to give everyone their own voice".
Prof Simon King, PersonalChair of Speech Processing, provides an introduction to how computers can be used generate natural-sounding speech.
He then introduces a method of automatically creating voices that sound like particular individuals, based on relatively short recordings of their voice. The method works for both normal speech and disordered speech.
The lecture concludes with a showcase of recent work from the Centre for Speech TechnologyResearch which includes the use of this technology to provide personalised communication aids for those who are losing the ability to speak, such as people with Motor Neurone Disease.
Recorded 6 February2012 at the Auditoriu...

published: 28 Feb 2012

Neural Network Learns to Generate Voice (RNN/LSTM)

[VOLUME WARNING] This is what happens when you throw raw audio (which happens to be a cute voice) into a neural network and then tell it to spit out what it's learned.
This is a recurrent neural network (LSTM type) with 3 layers of 680 neurons each, trying to find patterns in audio and reproduce them as well as it can. It's not a particularly big network considering the complexity and size of the data, mostly due to computing constraints, which makes me even more impressed with what it managed to do.
The audio that the network was learning from is voice actress Kanematsu Yuka voicing Hinata from Pure Pure. I used 11025 Hz, 8-bit audio because sound files get big quickly, at least compared to text files - 10 minutes already runs to 6.29MB, while that much plain text would take weeks or mo...

published: 24 May 2016

Neural Network Tries to Generate English Speech (RNN/LSTM)

By popular demand, I threw my own voice into a neural network (3 times) and got it to recreate what it had learned along the way!
This is 3 different recurrent neural networks (LSTM type) trying to find patterns in raw audio and reproduce them as well as they can. The networks are quite small considering the complexity of the data. I recorded 3 different vocal sessions as training data for the network, trying to get more impressive results out of the network each time. The audio is 8-bit and a low sample rate because sound files get very big very quickly, making the training of the network take a very long time. Well over 300 hours of training in total went into the experiments with my voice that led to this video.
The graphs are created from log files made during training, and show the...

published: 24 Dec 2016

BEST Text-To-Speech Voice (REAL HUMAN VOICE)

Julie's voice is so realistic,,,,
Thank you! for watching this video please leave a like if you enjoyed the video & Subscribe for more videos.
●DOWNLOAD Voice Packs Here ►https://goo.gl/gTvlqG
FOLLOW "kilObit" ON SOCIAL NETWORKS
FACEBOOK ►https://www.facebook.com/kil0bit
TWITTER ►https://twitter.com/kil0bit
WEBSITE ►http://goo.gl/qwCTD9
Visit the official Blog/Website of "kilObit' get everything in one place & the website looks cool, All the downloading things goes on my site so be sure to check out maybe you will find something helpful or entertaining there.
ADMINS SOCIAL NETWORK LINKS
▪FACEBOOK ►https://goo.gl/ebfmBo
▪TWITTER ►https://goo.gl/JkMX0p
▪INSTAGRAM ►https://goo.gl/SNmyt1
This is not a important part of the description but this is the person behind the "kilObit" you ...

What is SPEECH SYNTHESIS? What does SPEECH SYNTHESIS mean? SPEECH SYNTHESIS meaning - SPEECH SYNTHESIS definition - SPEECH SYNTHESIS explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ
Speech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech computer or speech synthesizer, and can be implemented in software or hardware products. A text-to-speech (TTS) system converts normal language text into speech; other systems render symbolic linguistic representations like phonetic transcriptions into speech.
Synthesized speech can be created by concatenating pieces of...

published: 20 Oct 2017

How does Text To Speech (TTS) work - by Acapela Voices

Welcome to this talking presentation! You are wondering what is the process to create a synthetic voice? And how does it work? Listen to this short story featuring some of our bright voices. This talking presentation uses SlideTalk online solution and Acapela Group voices.
More on http://www.slidetalk.net and http:www.acapela-group.com

published: 28 Feb 2014

DeepMind's WaveNet Text-To-Speech Algorithm

Today's artificial speech tends to sound robotic, but using a new system called WaveNet, Google Deepmind has created a new system that produces much more natural human speech. While not perfect, it is 50% better than current technologies. Since it is at core a general audio processor, it can also create music.
Find out more at: http://www.33rdsquare.com/2016/09/deepmind-uses-deep-neural-networks-to.html

published: 12 Sep 2016

Synthesizing Obama: Learning Lip Sync from Audio

Synthesizing Obama: Learning Lip Sync from Audio
Supasorn Suwajanakorn, Steven M. Seitz, Ira Kemelmacher-Shlizerman
SIGGRAPH 2017
Given audio of President Barack Obama, we synthesize a high quality video of him speaking with accurate lip sync, composited into a target video clip. Trained on many hours of his weekly address footage, a recurrent neural network learns the mapping from raw audio features to mouth shapes. Given the mouth shape at each time instant, we synthesize high quality mouth texture, and composite it with proper 3D pose matching to change what he appears to be saying in a target video to match the input audio track
http://grail.cs.washington.edu/projects/AudioToObama/

Arduino + Speakjet speech synthesis 'Nonsense Babbler'

What do you get if you cross an Arudino, a Speakjet IC and a bucket of verbal diarrhoea?
The first noise maker I've done based on a Speakjet. Proper text-to-speech is coming at some point down the line but I wanted to start with something odd.
Speakjet datasheet can be found here: https://www.sparkfun.com/datasheets/Components/General/speakjet-usermanual.pdf
The LEDs and reset button are done purely using built-in Speakjet features which are detailed in the datasheet.
The Arduino code I wrote can be found here: https://github.com/dannycarnage/SpeakjetNonsenseBabbler

published: 12 Feb 2017

C# SPEECH SYNTHESIS TUTORIAL

****** Inscreva-se: https://goo.gl/G4Ppnf ******
Descrição: In this Speech synthesis tutorial you're going to learn on how use the speech synthesis capability in your program in C#. To follow this tutorial you need to download and install the SpeechSDK5.1 in this link https://download.microsoft.com/download/B/4/3/B4314928-7B71-4336-9DE7-6FA4CF00B7B3/SpeechSDK51.exe after this you're good to go.
Patreon: https://goo.gl/A3iCR9
Twitter: https://twitter.com/pehlimaofficial
Python para iniciantes: https://www.youtube.com/playlist?list=PL39zyvnHdXh_osGdr5aMT5mj6cxNIiFlE
C# para iniciantes: https://www.youtube.com/playlist?list=PL39zyvnHdXh_iuHCk4bBKW-U5qOLfug8Z
F# para iniciantes: https://www.youtube.com/playlist?list=PL39zyvnHdXh8K9UgRWVmya_OIxyiPK8xA
FanPage: https://www.facebook...

published: 10 Oct 2017

Building speech synthesis systems for Indian languages by Prof.Hema Murthy, IITM

published: 10 Jul 2017

How to make an Ai 1.Speech Synthesis

Hi folks this is a video about how to make an AI
If you have not seen the Ai Video then go and check it its awesome
Check out my website to get the full code of the program
http://destyy.com/wo3DRC
1 video each week

published: 15 Apr 2017

009 Microvox - Vintage Computer Speech Synthesis

In the latest episode of Coke and Strippers we bring a 1980's MicroVox voice synthesizer back to life using an Arduino Uno.
The MicroVox uses a 6502 process to look up words in a small ROM dictionary to send the correct phonemes to the SC-01a speech synthesis chip, or you can send just the phonemes yourself.
If you have projects you'd like to see built, post them in the comments below.
MusicFireworks by Jahzzar is licensed under a Attribution-ShareAlike 3.0InternationalLicense.
http://freemusicarchive.org/music/Jahzzar/Travellers_Guide/Fireworks
Music: Hello Friend... by The Birth and Death of Silence is licensed under a Attribution License.
http://freemusicarchive.org/music/The_Birth_and_Death_of_Silence/The_Birth_and_Death_of_Silence/The_Birth_and_Death_of_Silence_-_Promo_-_03_Hel...

published: 18 Feb 2017

HMM-based Speech Synthesis: Fundamentals and Its Recent Advances

The task of speech synthesis is to convert normal language text into speech. In recent years, hidden Markov model (HMM) has been successfully applied to acoustic modeling for speech synthesis, and HMM-based parametric speech synthesis has become a mainstream speech synthesis method. This method is able to synthesize highly intelligible and smooth speech sounds. Another significant advantage of this model-based parametric approach is that it makes speech synthesis far more flexible compared to the conventional unit selection and waveform concatenation approach. This talk will first introduce the overall HMM synthesis system architecture developed at USTC. Then, some key techniques will be described, including the vocoder, acoustic modeling, parameter generation algorithm, MSD-HMM for F0 mo...

"Speech Synthesis," Kim Silverman

On Monday, April 16, Kim Silverman, principal research scientist at Apple, gave a talk at ICSI on speech synthesis, giving an overview of text normalization, na...

On Monday, April 16, Kim Silverman, principal research scientist at Apple, gave a talk at ICSI on speech synthesis, giving an overview of text normalization, named entity extraction, part-of-speech tagging, phrasing, topic tracking, pronunciation, intonation, duration, phonology, phonetics, and signal representation. Read the full abstract and bio at https://www.icsi.berkeley.edu/icsi/events/2012/04/kim-silverman-talk .
Title: "Speech Synthesis"
Abstract:
Conversion of text to speech requires processing at many levels of representation. This presentation will systematically step through text normalization, named entity extraction, part-of-speech tagging, phrasing, topic tracking, pronunciation, intonation, duration, phonology, phonetics, and signal representation. Examples of each stage, the difficulties encountered, and some typical approaches will be illustrated. This will provide a solid background for students to evaluate recent investigative approaches to HMM synthesis, and to modeling of speaker emotion.
Bio:
Kim Silverman is a principal research scientist at Apple, where among other things he led the development of the Alex text-to-speech synthesis system that is the flagship American English voice in OS X. He is first author on the ToBI standard for transcribing speech prosody.
Read the full bio at https://www.icsi.berkeley.edu/icsi/events/2012/04/kim-silverman-talk.

On Monday, April 16, Kim Silverman, principal research scientist at Apple, gave a talk at ICSI on speech synthesis, giving an overview of text normalization, named entity extraction, part-of-speech tagging, phrasing, topic tracking, pronunciation, intonation, duration, phonology, phonetics, and signal representation. Read the full abstract and bio at https://www.icsi.berkeley.edu/icsi/events/2012/04/kim-silverman-talk .
Title: "Speech Synthesis"
Abstract:
Conversion of text to speech requires processing at many levels of representation. This presentation will systematically step through text normalization, named entity extraction, part-of-speech tagging, phrasing, topic tracking, pronunciation, intonation, duration, phonology, phonetics, and signal representation. Examples of each stage, the difficulties encountered, and some typical approaches will be illustrated. This will provide a solid background for students to evaluate recent investigative approaches to HMM synthesis, and to modeling of speaker emotion.
Bio:
Kim Silverman is a principal research scientist at Apple, where among other things he led the development of the Alex text-to-speech synthesis system that is the flagship American English voice in OS X. He is first author on the ToBI standard for transcribing speech prosody.
Read the full bio at https://www.icsi.berkeley.edu/icsi/events/2012/04/kim-silverman-talk.

Generative Model-Based Text-to-Speech Synthesis

Heiga Zen, GoogleAbstract: Recent progress in generative modeling has improved the naturalness of synthesized speech significantly. In this talk I will summa...

Heiga Zen, GoogleAbstract: Recent progress in generative modeling has improved the naturalness of synthesized speech significantly. In this talk I will summarize these generative model-based approaches for speech synthesis and describe possible future directions.

Heiga Zen, GoogleAbstract: Recent progress in generative modeling has improved the naturalness of synthesized speech significantly. In this talk I will summarize these generative model-based approaches for speech synthesis and describe possible future directions.

Neural Network Learns to Generate Voice (RNN/LSTM)

[VOLUME WARNING] This is what happens when you throw raw audio (which happens to be a cute voice) into a neural network and then tell it to spit out what it's l...

[VOLUME WARNING] This is what happens when you throw raw audio (which happens to be a cute voice) into a neural network and then tell it to spit out what it's learned.
This is a recurrent neural network (LSTM type) with 3 layers of 680 neurons each, trying to find patterns in audio and reproduce them as well as it can. It's not a particularly big network considering the complexity and size of the data, mostly due to computing constraints, which makes me even more impressed with what it managed to do.
The audio that the network was learning from is voice actress Kanematsu Yuka voicing Hinata from Pure Pure. I used 11025 Hz, 8-bit audio because sound files get big quickly, at least compared to text files - 10 minutes already runs to 6.29MB, while that much plain text would take weeks or months for a human to read.
UPDATE: By popular demand, I have uploaded a video where I did this with male English voice, too: https://www.youtube.com/watch?v=NG-LATBZNBs
I was using the program "torch-rnn" (https://github.com/jcjohnson/torch-rnn/), which is actually designed to learn from and generate plain text. I wrote a program that converts any data into UTF-8 text and vice-versa, and to my excitement, torch-rnn happily processed that text as if there was nothing unusual. I did this because I don't know where to begin coding my own neural network program, but this workaround has some annoying restraints. E.g. torch-rnn doesn't like to output more than about 300KB of data, hence all generated sounds being only ~27 seconds long.
It took roughly 29 hours to train the network to ~35 epochs (74,000 iterations) and over 12 hours to generate the samples (output audio). These times are quite approximate as the same server was both training and sampling (from past network "checkpoints") at the same time, which slowed it down. Huge thanks go to Melan for letting me use his server for this fun project! Let's try a bigger network next time, if you can stand waiting an hour for 27 seconds of potentially-useless audio. xD
I feel that my target audience couldn't possibly get any smaller than it is right now...EDIT: I have put some graphs of the training and validation losses on my blog for those who have asked what the losses were!
http://robbi-985.homeip.net/blog/?p=1760#settings
EDIT 2: I have been asked several times about my binary-to-UTF-8 program. The program basically substitutes any raw byte value for a valid UTF-8 encoding of a character. So after conversion, there'll be a maximum of 256 unique UTF-8 characters. I threw the program together in VB6, so it will only run on Windows. However, I rewrote all the important code in a C++-like pseudocode:
http://robbi-985.homeip.net/information/bintoutf8_pseudo.txt
Also, here is an English explanation of how my binary-to-UTF-8 program works:
http://robbi-985.homeip.net/information/bintoutf8_info.txt
EDIT 3: I have released my BinToUTF8 program to the public! Please have a look here:
http://robbi-985.homeip.net/blog/?p=1845

[VOLUME WARNING] This is what happens when you throw raw audio (which happens to be a cute voice) into a neural network and then tell it to spit out what it's learned.
This is a recurrent neural network (LSTM type) with 3 layers of 680 neurons each, trying to find patterns in audio and reproduce them as well as it can. It's not a particularly big network considering the complexity and size of the data, mostly due to computing constraints, which makes me even more impressed with what it managed to do.
The audio that the network was learning from is voice actress Kanematsu Yuka voicing Hinata from Pure Pure. I used 11025 Hz, 8-bit audio because sound files get big quickly, at least compared to text files - 10 minutes already runs to 6.29MB, while that much plain text would take weeks or months for a human to read.
UPDATE: By popular demand, I have uploaded a video where I did this with male English voice, too: https://www.youtube.com/watch?v=NG-LATBZNBs
I was using the program "torch-rnn" (https://github.com/jcjohnson/torch-rnn/), which is actually designed to learn from and generate plain text. I wrote a program that converts any data into UTF-8 text and vice-versa, and to my excitement, torch-rnn happily processed that text as if there was nothing unusual. I did this because I don't know where to begin coding my own neural network program, but this workaround has some annoying restraints. E.g. torch-rnn doesn't like to output more than about 300KB of data, hence all generated sounds being only ~27 seconds long.
It took roughly 29 hours to train the network to ~35 epochs (74,000 iterations) and over 12 hours to generate the samples (output audio). These times are quite approximate as the same server was both training and sampling (from past network "checkpoints") at the same time, which slowed it down. Huge thanks go to Melan for letting me use his server for this fun project! Let's try a bigger network next time, if you can stand waiting an hour for 27 seconds of potentially-useless audio. xD
I feel that my target audience couldn't possibly get any smaller than it is right now...EDIT: I have put some graphs of the training and validation losses on my blog for those who have asked what the losses were!
http://robbi-985.homeip.net/blog/?p=1760#settings
EDIT 2: I have been asked several times about my binary-to-UTF-8 program. The program basically substitutes any raw byte value for a valid UTF-8 encoding of a character. So after conversion, there'll be a maximum of 256 unique UTF-8 characters. I threw the program together in VB6, so it will only run on Windows. However, I rewrote all the important code in a C++-like pseudocode:
http://robbi-985.homeip.net/information/bintoutf8_pseudo.txt
Also, here is an English explanation of how my binary-to-UTF-8 program works:
http://robbi-985.homeip.net/information/bintoutf8_info.txt
EDIT 3: I have released my BinToUTF8 program to the public! Please have a look here:
http://robbi-985.homeip.net/blog/?p=1845

Neural Network Tries to Generate English Speech (RNN/LSTM)

By popular demand, I threw my own voice into a neural network (3 times) and got it to recreate what it had learned along the way!
This is 3 different recurren...

By popular demand, I threw my own voice into a neural network (3 times) and got it to recreate what it had learned along the way!
This is 3 different recurrent neural networks (LSTM type) trying to find patterns in raw audio and reproduce them as well as they can. The networks are quite small considering the complexity of the data. I recorded 3 different vocal sessions as training data for the network, trying to get more impressive results out of the network each time. The audio is 8-bit and a low sample rate because sound files get very big very quickly, making the training of the network take a very long time. Well over 300 hours of training in total went into the experiments with my voice that led to this video.
The graphs are created from log files made during training, and show the progress that it was making leading up to immediately before the audio that you hear at every point in the video. Their scrolling speeds up at points where I only show a short sample of the sound, because I wanted to dedicated more time to the more impressive parts. I included a lot of information in the video itself where it's relevant (and at the end), especially details about each of the 3 neural networks at the beginning of each of the 3 sections, so please be sure to check that if you'd like more details.
I'm less happy with the results this time around than in my last RNN+voice video (https://www.youtube.com/watch?v=FsVSZpoUdSU), because I've experimented much less with my own voice than I have with higher-pitched voices from various games and haven't found the ideal combination of settings yet. That's because I don't really want to hear the sound of my own voice, but so many people commented on my old video that they wanted to hear a neural network trained on a male English voice, so here we are now! Also, learning from a low-pitched voice is not as easy as with a high-pitched voice, for reasons explained in the first part of the video (basically, the most fundamental patterns are longer with a low-pitched voice).
The neural network software is the open-source "torch-rnn" (https://github.com/jcjohnson/torch-rnn/), although that is only designed to learn from plain text. Frankly, I'm still amazed at what a good job it does of learning from raw audio, with many overlapping patterns over longer timeframes than text. I made a program(*) that substitutes raw bytes in any file (e.g. audio) for valid UTF-8 text characters and torch-rnn happily learned from it. My program also substituted torch-rnn's generated text back into raw bytes to get audio again. I do not understand the mathematics and low-level algorithms that go make a neural network work, and I cannot program my own, so please check the code and .md files at torch-rnn's Github page for details. Also, torch-rnn is actually a more-efficient fork of an earlier software called char-rnn (https://github.com/karpathy/char-rnn), whose project page also has a lot of useful information.
I will probably soon release the program that I wrote to create the line graphs from CSV files. It can make images up to 16383 pixels wide/tall with customisable colours, from CSV files with hundreds of thousands of lines, in a few seconds. All free software I could find failed hideously at this (e.g. OpenOffice Calc took over a minute to refresh the screen with only a fraction of that many lines, during which time it stopped responding; the lines overlapped in an ugly way that meant you couldn't even see the average value; and "exporting" graphs is limited to pressing Print Screen, so you're limited to the width of your screen... really?).
(*)Here is the code rewritten from VB6 in a C++-like pseudocode:
http://robbi-985.homeip.net/information/bintoutf8_pseudo.txt
Also, here is an English explanation of the idea behind how it works:
http://robbi-985.homeip.net/information/bintoutf8_info.txt
EDIT: I have released my BinToUTF8 program to the public! Please have a look here:
http://robbi-985.homeip.net/blog/?p=1845

By popular demand, I threw my own voice into a neural network (3 times) and got it to recreate what it had learned along the way!
This is 3 different recurrent neural networks (LSTM type) trying to find patterns in raw audio and reproduce them as well as they can. The networks are quite small considering the complexity of the data. I recorded 3 different vocal sessions as training data for the network, trying to get more impressive results out of the network each time. The audio is 8-bit and a low sample rate because sound files get very big very quickly, making the training of the network take a very long time. Well over 300 hours of training in total went into the experiments with my voice that led to this video.
The graphs are created from log files made during training, and show the progress that it was making leading up to immediately before the audio that you hear at every point in the video. Their scrolling speeds up at points where I only show a short sample of the sound, because I wanted to dedicated more time to the more impressive parts. I included a lot of information in the video itself where it's relevant (and at the end), especially details about each of the 3 neural networks at the beginning of each of the 3 sections, so please be sure to check that if you'd like more details.
I'm less happy with the results this time around than in my last RNN+voice video (https://www.youtube.com/watch?v=FsVSZpoUdSU), because I've experimented much less with my own voice than I have with higher-pitched voices from various games and haven't found the ideal combination of settings yet. That's because I don't really want to hear the sound of my own voice, but so many people commented on my old video that they wanted to hear a neural network trained on a male English voice, so here we are now! Also, learning from a low-pitched voice is not as easy as with a high-pitched voice, for reasons explained in the first part of the video (basically, the most fundamental patterns are longer with a low-pitched voice).
The neural network software is the open-source "torch-rnn" (https://github.com/jcjohnson/torch-rnn/), although that is only designed to learn from plain text. Frankly, I'm still amazed at what a good job it does of learning from raw audio, with many overlapping patterns over longer timeframes than text. I made a program(*) that substitutes raw bytes in any file (e.g. audio) for valid UTF-8 text characters and torch-rnn happily learned from it. My program also substituted torch-rnn's generated text back into raw bytes to get audio again. I do not understand the mathematics and low-level algorithms that go make a neural network work, and I cannot program my own, so please check the code and .md files at torch-rnn's Github page for details. Also, torch-rnn is actually a more-efficient fork of an earlier software called char-rnn (https://github.com/karpathy/char-rnn), whose project page also has a lot of useful information.
I will probably soon release the program that I wrote to create the line graphs from CSV files. It can make images up to 16383 pixels wide/tall with customisable colours, from CSV files with hundreds of thousands of lines, in a few seconds. All free software I could find failed hideously at this (e.g. OpenOffice Calc took over a minute to refresh the screen with only a fraction of that many lines, during which time it stopped responding; the lines overlapped in an ugly way that meant you couldn't even see the average value; and "exporting" graphs is limited to pressing Print Screen, so you're limited to the width of your screen... really?).
(*)Here is the code rewritten from VB6 in a C++-like pseudocode:
http://robbi-985.homeip.net/information/bintoutf8_pseudo.txt
Also, here is an English explanation of the idea behind how it works:
http://robbi-985.homeip.net/information/bintoutf8_info.txt
EDIT: I have released my BinToUTF8 program to the public! Please have a look here:
http://robbi-985.homeip.net/blog/?p=1845

BEST Text-To-Speech Voice (REAL HUMAN VOICE)

Julie's voice is so realistic,,,,
Thank you! for watching this video please leave a like if you enjoyed the video & Subscribe for more videos.
●DOWNLOAD Voice...

Julie's voice is so realistic,,,,
Thank you! for watching this video please leave a like if you enjoyed the video & Subscribe for more videos.
●DOWNLOAD Voice Packs Here ►https://goo.gl/gTvlqG
FOLLOW "kilObit" ON SOCIAL NETWORKS
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TWITTER ►https://twitter.com/kil0bit
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Visit the official Blog/Website of "kilObit' get everything in one place & the website looks cool, All the downloading things goes on my site so be sure to check out maybe you will find something helpful or entertaining there.
ADMINS SOCIAL NETWORK LINKS
▪FACEBOOK ►https://goo.gl/ebfmBo
▪TWITTER ►https://goo.gl/JkMX0p
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This is not a important part of the description but this is the person behind the "kilObit" you may wanna know him.
Thank You! for all of your support...

Julie's voice is so realistic,,,,
Thank you! for watching this video please leave a like if you enjoyed the video & Subscribe for more videos.
●DOWNLOAD Voice Packs Here ►https://goo.gl/gTvlqG
FOLLOW "kilObit" ON SOCIAL NETWORKS
FACEBOOK ►https://www.facebook.com/kil0bit
TWITTER ►https://twitter.com/kil0bit
WEBSITE ►http://goo.gl/qwCTD9
Visit the official Blog/Website of "kilObit' get everything in one place & the website looks cool, All the downloading things goes on my site so be sure to check out maybe you will find something helpful or entertaining there.
ADMINS SOCIAL NETWORK LINKS
▪FACEBOOK ►https://goo.gl/ebfmBo
▪TWITTER ►https://goo.gl/JkMX0p
▪INSTAGRAM ►https://goo.gl/SNmyt1
This is not a important part of the description but this is the person behind the "kilObit" you may wanna know him.
Thank You! for all of your support...

What is SPEECH SYNTHESIS? What does SPEECH SYNTHESIS mean? SPEECH SYNTHESIS meaning - SPEECH SYNTHESIS definition - SPEECH SYNTHESIS explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ
Speech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech computer or speech synthesizer, and can be implemented in software or hardware products. A text-to-speech (TTS) system converts normal language text into speech; other systems render symbolic linguistic representations like phonetic transcriptions into speech.
Synthesized speech can be created by concatenating pieces of recorded speech that are stored in a database. Systems differ in the size of the stored speech units; a system that stores phones or diphones provides the largest output range, but may lack clarity. For specific usage domains, the storage of entire words or sentences allows for high-quality output. Alternatively, a synthesizer can incorporate a model of the vocal tract and other human voice characteristics to create a completely "synthetic" voice output.
The quality of a speech synthesizer is judged by its similarity to the human voice and by its ability to be understood clearly. An intelligible text-to-speech program allows people with visual impairments or reading disabilities to listen to written words on a home computer. Many computer operating systems have included speech synthesizers since the early 1990s.
A text-to-speech system (or "engine") is composed of two parts: a front-end and a back-end. The front-end has two major tasks. First, it converts raw text containing symbols like numbers and abbreviations into the equivalent of written-out words. This process is often called text normalization, pre-processing, or tokenization. The front-end then assigns phonetic transcriptions to each word, and divides and marks the text into prosodic units, like phrases, clauses, and sentences. The process of assigning phonetic transcriptions to words is called text-to-phoneme or grapheme-to-phoneme conversion. Phonetic transcriptions and prosody information together make up the symbolic linguistic representation that is output by the front-end. The back-end—often referred to as the synthesizer—then converts the symbolic linguistic representation into sound. In certain systems, this part includes the computation of the target prosody (pitch contour, phoneme durations), which is then imposed on the output speech.

What is SPEECH SYNTHESIS? What does SPEECH SYNTHESIS mean? SPEECH SYNTHESIS meaning - SPEECH SYNTHESIS definition - SPEECH SYNTHESIS explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ
Speech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech computer or speech synthesizer, and can be implemented in software or hardware products. A text-to-speech (TTS) system converts normal language text into speech; other systems render symbolic linguistic representations like phonetic transcriptions into speech.
Synthesized speech can be created by concatenating pieces of recorded speech that are stored in a database. Systems differ in the size of the stored speech units; a system that stores phones or diphones provides the largest output range, but may lack clarity. For specific usage domains, the storage of entire words or sentences allows for high-quality output. Alternatively, a synthesizer can incorporate a model of the vocal tract and other human voice characteristics to create a completely "synthetic" voice output.
The quality of a speech synthesizer is judged by its similarity to the human voice and by its ability to be understood clearly. An intelligible text-to-speech program allows people with visual impairments or reading disabilities to listen to written words on a home computer. Many computer operating systems have included speech synthesizers since the early 1990s.
A text-to-speech system (or "engine") is composed of two parts: a front-end and a back-end. The front-end has two major tasks. First, it converts raw text containing symbols like numbers and abbreviations into the equivalent of written-out words. This process is often called text normalization, pre-processing, or tokenization. The front-end then assigns phonetic transcriptions to each word, and divides and marks the text into prosodic units, like phrases, clauses, and sentences. The process of assigning phonetic transcriptions to words is called text-to-phoneme or grapheme-to-phoneme conversion. Phonetic transcriptions and prosody information together make up the symbolic linguistic representation that is output by the front-end. The back-end—often referred to as the synthesizer—then converts the symbolic linguistic representation into sound. In certain systems, this part includes the computation of the target prosody (pitch contour, phoneme durations), which is then imposed on the output speech.

How does Text To Speech (TTS) work - by Acapela Voices

Welcome to this talking presentation! You are wondering what is the process to create a synthetic voice? And how does it work? Listen to this short story featu...

Welcome to this talking presentation! You are wondering what is the process to create a synthetic voice? And how does it work? Listen to this short story featuring some of our bright voices. This talking presentation uses SlideTalk online solution and Acapela Group voices.
More on http://www.slidetalk.net and http:www.acapela-group.com

Welcome to this talking presentation! You are wondering what is the process to create a synthetic voice? And how does it work? Listen to this short story featuring some of our bright voices. This talking presentation uses SlideTalk online solution and Acapela Group voices.
More on http://www.slidetalk.net and http:www.acapela-group.com

DeepMind's WaveNet Text-To-Speech Algorithm

Today's artificial speech tends to sound robotic, but using a new system called WaveNet, Google Deepmind has created a new system that produces much more natura...

Today's artificial speech tends to sound robotic, but using a new system called WaveNet, Google Deepmind has created a new system that produces much more natural human speech. While not perfect, it is 50% better than current technologies. Since it is at core a general audio processor, it can also create music.
Find out more at: http://www.33rdsquare.com/2016/09/deepmind-uses-deep-neural-networks-to.html

Today's artificial speech tends to sound robotic, but using a new system called WaveNet, Google Deepmind has created a new system that produces much more natural human speech. While not perfect, it is 50% better than current technologies. Since it is at core a general audio processor, it can also create music.
Find out more at: http://www.33rdsquare.com/2016/09/deepmind-uses-deep-neural-networks-to.html

Synthesizing Obama: Learning Lip Sync from Audio
Supasorn Suwajanakorn, Steven M. Seitz, Ira Kemelmacher-Shlizerman
SIGGRAPH 2017
Given audio of President Barack Obama, we synthesize a high quality video of him speaking with accurate lip sync, composited into a target video clip. Trained on many hours of his weekly address footage, a recurrent neural network learns the mapping from raw audio features to mouth shapes. Given the mouth shape at each time instant, we synthesize high quality mouth texture, and composite it with proper 3D pose matching to change what he appears to be saying in a target video to match the input audio track
http://grail.cs.washington.edu/projects/AudioToObama/

Synthesizing Obama: Learning Lip Sync from Audio
Supasorn Suwajanakorn, Steven M. Seitz, Ira Kemelmacher-Shlizerman
SIGGRAPH 2017
Given audio of President Barack Obama, we synthesize a high quality video of him speaking with accurate lip sync, composited into a target video clip. Trained on many hours of his weekly address footage, a recurrent neural network learns the mapping from raw audio features to mouth shapes. Given the mouth shape at each time instant, we synthesize high quality mouth texture, and composite it with proper 3D pose matching to change what he appears to be saying in a target video to match the input audio track
http://grail.cs.washington.edu/projects/AudioToObama/

Arduino + Speakjet speech synthesis 'Nonsense Babbler'

What do you get if you cross an Arudino, a Speakjet IC and a bucket of verbal diarrhoea?
The first noise maker I've done based on a Speakjet. Proper text-to-sp...

What do you get if you cross an Arudino, a Speakjet IC and a bucket of verbal diarrhoea?
The first noise maker I've done based on a Speakjet. Proper text-to-speech is coming at some point down the line but I wanted to start with something odd.
Speakjet datasheet can be found here: https://www.sparkfun.com/datasheets/Components/General/speakjet-usermanual.pdf
The LEDs and reset button are done purely using built-in Speakjet features which are detailed in the datasheet.
The Arduino code I wrote can be found here: https://github.com/dannycarnage/SpeakjetNonsenseBabbler

What do you get if you cross an Arudino, a Speakjet IC and a bucket of verbal diarrhoea?
The first noise maker I've done based on a Speakjet. Proper text-to-speech is coming at some point down the line but I wanted to start with something odd.
Speakjet datasheet can be found here: https://www.sparkfun.com/datasheets/Components/General/speakjet-usermanual.pdf
The LEDs and reset button are done purely using built-in Speakjet features which are detailed in the datasheet.
The Arduino code I wrote can be found here: https://github.com/dannycarnage/SpeakjetNonsenseBabbler

C# SPEECH SYNTHESIS TUTORIAL

****** Inscreva-se: https://goo.gl/G4Ppnf ******
Descrição: In this Speech synthesis tutorial you're going to learn on how use the speech synthesis capabil...

****** Inscreva-se: https://goo.gl/G4Ppnf ******
Descrição: In this Speech synthesis tutorial you're going to learn on how use the speech synthesis capability in your program in C#. To follow this tutorial you need to download and install the SpeechSDK5.1 in this link https://download.microsoft.com/download/B/4/3/B4314928-7B71-4336-9DE7-6FA4CF00B7B3/SpeechSDK51.exe after this you're good to go.
Patreon: https://goo.gl/A3iCR9
Twitter: https://twitter.com/pehlimaofficial
Python para iniciantes: https://www.youtube.com/playlist?list=PL39zyvnHdXh_osGdr5aMT5mj6cxNIiFlE
C# para iniciantes: https://www.youtube.com/playlist?list=PL39zyvnHdXh_iuHCk4bBKW-U5qOLfug8Z
F# para iniciantes: https://www.youtube.com/playlist?list=PL39zyvnHdXh8K9UgRWVmya_OIxyiPK8xA
FanPage: https://www.facebook.com/C%C3%B3digo-Logo-598277996996624/?ref=bookmarks
CódigoShare: https://www.facebook.com/groups/633347333472123/?ref=bookmarks

****** Inscreva-se: https://goo.gl/G4Ppnf ******
Descrição: In this Speech synthesis tutorial you're going to learn on how use the speech synthesis capability in your program in C#. To follow this tutorial you need to download and install the SpeechSDK5.1 in this link https://download.microsoft.com/download/B/4/3/B4314928-7B71-4336-9DE7-6FA4CF00B7B3/SpeechSDK51.exe after this you're good to go.
Patreon: https://goo.gl/A3iCR9
Twitter: https://twitter.com/pehlimaofficial
Python para iniciantes: https://www.youtube.com/playlist?list=PL39zyvnHdXh_osGdr5aMT5mj6cxNIiFlE
C# para iniciantes: https://www.youtube.com/playlist?list=PL39zyvnHdXh_iuHCk4bBKW-U5qOLfug8Z
F# para iniciantes: https://www.youtube.com/playlist?list=PL39zyvnHdXh8K9UgRWVmya_OIxyiPK8xA
FanPage: https://www.facebook.com/C%C3%B3digo-Logo-598277996996624/?ref=bookmarks
CódigoShare: https://www.facebook.com/groups/633347333472123/?ref=bookmarks

published:10 Oct 2017

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Building speech synthesis systems for Indian languages by Prof.Hema Murthy, IITM

How to make an Ai 1.Speech Synthesis

Hi folks this is a video about how to make an AI
If you have not seen the Ai Video then go and check it its awesome
Check out my website to get the full co...

Hi folks this is a video about how to make an AI
If you have not seen the Ai Video then go and check it its awesome
Check out my website to get the full code of the program
http://destyy.com/wo3DRC
1 video each week

Hi folks this is a video about how to make an AI
If you have not seen the Ai Video then go and check it its awesome
Check out my website to get the full code of the program
http://destyy.com/wo3DRC
1 video each week

009 Microvox - Vintage Computer Speech Synthesis

In the latest episode of Coke and Strippers we bring a 1980's MicroVox voice synthesizer back to life using an Arduino Uno.
The MicroVox uses a 6502 process t...

In the latest episode of Coke and Strippers we bring a 1980's MicroVox voice synthesizer back to life using an Arduino Uno.
The MicroVox uses a 6502 process to look up words in a small ROM dictionary to send the correct phonemes to the SC-01a speech synthesis chip, or you can send just the phonemes yourself.
If you have projects you'd like to see built, post them in the comments below.
MusicFireworks by Jahzzar is licensed under a Attribution-ShareAlike 3.0InternationalLicense.
http://freemusicarchive.org/music/Jahzzar/Travellers_Guide/Fireworks
Music: Hello Friend... by The Birth and Death of Silence is licensed under a Attribution License.
http://freemusicarchive.org/music/The_Birth_and_Death_of_Silence/The_Birth_and_Death_of_Silence/The_Birth_and_Death_of_Silence_-_Promo_-_03_Hello_Friend
Music: Hello Friend... by The Birth and Death of Silence is licensed under a Attribution License.
Music:The Birth and Death of Silence by The Birth and Death of Silence is licensed under a Attribution License.
http://freemusicarchive.org/music/The_Birth_and_Death_of_Silence/The_Birth_and_Death_of_Silence/

In the latest episode of Coke and Strippers we bring a 1980's MicroVox voice synthesizer back to life using an Arduino Uno.
The MicroVox uses a 6502 process to look up words in a small ROM dictionary to send the correct phonemes to the SC-01a speech synthesis chip, or you can send just the phonemes yourself.
If you have projects you'd like to see built, post them in the comments below.
MusicFireworks by Jahzzar is licensed under a Attribution-ShareAlike 3.0InternationalLicense.
http://freemusicarchive.org/music/Jahzzar/Travellers_Guide/Fireworks
Music: Hello Friend... by The Birth and Death of Silence is licensed under a Attribution License.
http://freemusicarchive.org/music/The_Birth_and_Death_of_Silence/The_Birth_and_Death_of_Silence/The_Birth_and_Death_of_Silence_-_Promo_-_03_Hello_Friend
Music: Hello Friend... by The Birth and Death of Silence is licensed under a Attribution License.
Music:The Birth and Death of Silence by The Birth and Death of Silence is licensed under a Attribution License.
http://freemusicarchive.org/music/The_Birth_and_Death_of_Silence/The_Birth_and_Death_of_Silence/

HMM-based Speech Synthesis: Fundamentals and Its Recent Advances

The task of speech synthesis is to convert normal language text into speech. In recent years, hidden Markov model (HMM) has been successfully applied to acousti...

The task of speech synthesis is to convert normal language text into speech. In recent years, hidden Markov model (HMM) has been successfully applied to acoustic modeling for speech synthesis, and HMM-based parametric speech synthesis has become a mainstream speech synthesis method. This method is able to synthesize highly intelligible and smooth speech sounds. Another significant advantage of this model-based parametric approach is that it makes speech synthesis far more flexible compared to the conventional unit selection and waveform concatenation approach. This talk will first introduce the overall HMM synthesis system architecture developed at USTC. Then, some key techniques will be described, including the vocoder, acoustic modeling, parameter generation algorithm, MSD-HMM for F0 modeling, context-dependent model training, etc. Our method will be compared with the unit selection approach and its flexibility in controlling voice characteristics will also be presented. The second part of this talk will describe some recent advances of HMM-based speech synthesis at the USTC speech group. The methods to be described include: 1) articulatory control of HMM-based speech synthesis, which further improves the flexibility of HMM-based speech synthesis by integrating phonetic knowledge, 2) LPS-GV and minimum KLD based parameter generation, which alleviates the over-smoothing of generated spectral features and improves the naturalness of synthetic speech, and 3) hybrid HMM-based/unit-selection approach which achieves excellent performance in the BlizzardChallenge speech synthesis evaluation events of recent years.

The task of speech synthesis is to convert normal language text into speech. In recent years, hidden Markov model (HMM) has been successfully applied to acoustic modeling for speech synthesis, and HMM-based parametric speech synthesis has become a mainstream speech synthesis method. This method is able to synthesize highly intelligible and smooth speech sounds. Another significant advantage of this model-based parametric approach is that it makes speech synthesis far more flexible compared to the conventional unit selection and waveform concatenation approach. This talk will first introduce the overall HMM synthesis system architecture developed at USTC. Then, some key techniques will be described, including the vocoder, acoustic modeling, parameter generation algorithm, MSD-HMM for F0 modeling, context-dependent model training, etc. Our method will be compared with the unit selection approach and its flexibility in controlling voice characteristics will also be presented. The second part of this talk will describe some recent advances of HMM-based speech synthesis at the USTC speech group. The methods to be described include: 1) articulatory control of HMM-based speech synthesis, which further improves the flexibility of HMM-based speech synthesis by integrating phonetic knowledge, 2) LPS-GV and minimum KLD based parameter generation, which alleviates the over-smoothing of generated spectral features and improves the naturalness of synthetic speech, and 3) hybrid HMM-based/unit-selection approach which achieves excellent performance in the BlizzardChallenge speech synthesis evaluation events of recent years.

"Speech Synthesis," Kim Silverman

On Monday, April 16, Kim Silverman, principal research scientist at Apple, gave a talk at ICSI on speech synthesis, giving an overview of text normalization, named entity extraction, part-of-speech tagging, phrasing, topic tracking, pronunciation, intonation, duration, phonology, phonetics, and signal representation. Read the full abstract and bio at https://www.icsi.berkeley.edu/icsi/events/2012/04/kim-silverman-talk .
Title: "Speech Synthesis"
Abstract:
Conversion of text to speech requires processing at many levels of representation. This presentation will systematically step through text normalization, named entity extraction, part-of-speech tagging, phrasing, topic tracking, pronunciation, intonation, duration, phonology, phonetics, and signal representation. Examples of each stage, the difficulties encountered, and some typical approaches will be illustrated. This will provide a solid background for students to evaluate recent investigative approaches to HMM synthesis, and to modeling of speaker emotion.
Bio:
Kim Silverman is a principal research scientist at Apple, where among other things he led the development of the Alex text-to-speech synthesis system that is the flagship American English voice in OS X. He is first author on the ToBI standard for transcribing speech prosody.
Read the full bio at https://www.icsi.berkeley.edu/icsi/events/2012/04/kim-silverman-talk.

Generative Model-Based Text-to-Speech Synthesis

Heiga Zen, GoogleAbstract: Recent progress in generative modeling has improved the naturalness of synthesized speech significantly. In this talk I will summarize these generative model-based approaches for speech synthesis and describe possible future directions.

Neural Network Learns to Generate Voice (RNN/LSTM)

[VOLUME WARNING] This is what happens when you throw raw audio (which happens to be a cute voice) into a neural network and then tell it to spit out what it's learned.
This is a recurrent neural network (LSTM type) with 3 layers of 680 neurons each, trying to find patterns in audio and reproduce them as well as it can. It's not a particularly big network considering the complexity and size of the data, mostly due to computing constraints, which makes me even more impressed with what it managed to do.
The audio that the network was learning from is voice actress Kanematsu Yuka voicing Hinata from Pure Pure. I used 11025 Hz, 8-bit audio because sound files get big quickly, at least compared to text files - 10 minutes already runs to 6.29MB, while that much plain text would take weeks or months for a human to read.
UPDATE: By popular demand, I have uploaded a video where I did this with male English voice, too: https://www.youtube.com/watch?v=NG-LATBZNBs
I was using the program "torch-rnn" (https://github.com/jcjohnson/torch-rnn/), which is actually designed to learn from and generate plain text. I wrote a program that converts any data into UTF-8 text and vice-versa, and to my excitement, torch-rnn happily processed that text as if there was nothing unusual. I did this because I don't know where to begin coding my own neural network program, but this workaround has some annoying restraints. E.g. torch-rnn doesn't like to output more than about 300KB of data, hence all generated sounds being only ~27 seconds long.
It took roughly 29 hours to train the network to ~35 epochs (74,000 iterations) and over 12 hours to generate the samples (output audio). These times are quite approximate as the same server was both training and sampling (from past network "checkpoints") at the same time, which slowed it down. Huge thanks go to Melan for letting me use his server for this fun project! Let's try a bigger network next time, if you can stand waiting an hour for 27 seconds of potentially-useless audio. xD
I feel that my target audience couldn't possibly get any smaller than it is right now...EDIT: I have put some graphs of the training and validation losses on my blog for those who have asked what the losses were!
http://robbi-985.homeip.net/blog/?p=1760#settings
EDIT 2: I have been asked several times about my binary-to-UTF-8 program. The program basically substitutes any raw byte value for a valid UTF-8 encoding of a character. So after conversion, there'll be a maximum of 256 unique UTF-8 characters. I threw the program together in VB6, so it will only run on Windows. However, I rewrote all the important code in a C++-like pseudocode:
http://robbi-985.homeip.net/information/bintoutf8_pseudo.txt
Also, here is an English explanation of how my binary-to-UTF-8 program works:
http://robbi-985.homeip.net/information/bintoutf8_info.txt
EDIT 3: I have released my BinToUTF8 program to the public! Please have a look here:
http://robbi-985.homeip.net/blog/?p=1845

13:41

Neural Network Tries to Generate English Speech (RNN/LSTM)

By popular demand, I threw my own voice into a neural network (3 times) and got it to recr...

Neural Network Tries to Generate English Speech (RNN/LSTM)

By popular demand, I threw my own voice into a neural network (3 times) and got it to recreate what it had learned along the way!
This is 3 different recurrent neural networks (LSTM type) trying to find patterns in raw audio and reproduce them as well as they can. The networks are quite small considering the complexity of the data. I recorded 3 different vocal sessions as training data for the network, trying to get more impressive results out of the network each time. The audio is 8-bit and a low sample rate because sound files get very big very quickly, making the training of the network take a very long time. Well over 300 hours of training in total went into the experiments with my voice that led to this video.
The graphs are created from log files made during training, and show the progress that it was making leading up to immediately before the audio that you hear at every point in the video. Their scrolling speeds up at points where I only show a short sample of the sound, because I wanted to dedicated more time to the more impressive parts. I included a lot of information in the video itself where it's relevant (and at the end), especially details about each of the 3 neural networks at the beginning of each of the 3 sections, so please be sure to check that if you'd like more details.
I'm less happy with the results this time around than in my last RNN+voice video (https://www.youtube.com/watch?v=FsVSZpoUdSU), because I've experimented much less with my own voice than I have with higher-pitched voices from various games and haven't found the ideal combination of settings yet. That's because I don't really want to hear the sound of my own voice, but so many people commented on my old video that they wanted to hear a neural network trained on a male English voice, so here we are now! Also, learning from a low-pitched voice is not as easy as with a high-pitched voice, for reasons explained in the first part of the video (basically, the most fundamental patterns are longer with a low-pitched voice).
The neural network software is the open-source "torch-rnn" (https://github.com/jcjohnson/torch-rnn/), although that is only designed to learn from plain text. Frankly, I'm still amazed at what a good job it does of learning from raw audio, with many overlapping patterns over longer timeframes than text. I made a program(*) that substitutes raw bytes in any file (e.g. audio) for valid UTF-8 text characters and torch-rnn happily learned from it. My program also substituted torch-rnn's generated text back into raw bytes to get audio again. I do not understand the mathematics and low-level algorithms that go make a neural network work, and I cannot program my own, so please check the code and .md files at torch-rnn's Github page for details. Also, torch-rnn is actually a more-efficient fork of an earlier software called char-rnn (https://github.com/karpathy/char-rnn), whose project page also has a lot of useful information.
I will probably soon release the program that I wrote to create the line graphs from CSV files. It can make images up to 16383 pixels wide/tall with customisable colours, from CSV files with hundreds of thousands of lines, in a few seconds. All free software I could find failed hideously at this (e.g. OpenOffice Calc took over a minute to refresh the screen with only a fraction of that many lines, during which time it stopped responding; the lines overlapped in an ugly way that meant you couldn't even see the average value; and "exporting" graphs is limited to pressing Print Screen, so you're limited to the width of your screen... really?).
(*)Here is the code rewritten from VB6 in a C++-like pseudocode:
http://robbi-985.homeip.net/information/bintoutf8_pseudo.txt
Also, here is an English explanation of the idea behind how it works:
http://robbi-985.homeip.net/information/bintoutf8_info.txt
EDIT: I have released my BinToUTF8 program to the public! Please have a look here:
http://robbi-985.homeip.net/blog/?p=1845

5:18

BEST Text-To-Speech Voice (REAL HUMAN VOICE)

Julie's voice is so realistic,,,,
Thank you! for watching this video please leave a like ...

BEST Text-To-Speech Voice (REAL HUMAN VOICE)

Julie's voice is so realistic,,,,
Thank you! for watching this video please leave a like if you enjoyed the video & Subscribe for more videos.
●DOWNLOAD Voice Packs Here ►https://goo.gl/gTvlqG
FOLLOW "kilObit" ON SOCIAL NETWORKS
FACEBOOK ►https://www.facebook.com/kil0bit
TWITTER ►https://twitter.com/kil0bit
WEBSITE ►http://goo.gl/qwCTD9
Visit the official Blog/Website of "kilObit' get everything in one place & the website looks cool, All the downloading things goes on my site so be sure to check out maybe you will find something helpful or entertaining there.
ADMINS SOCIAL NETWORK LINKS
▪FACEBOOK ►https://goo.gl/ebfmBo
▪TWITTER ►https://goo.gl/JkMX0p
▪INSTAGRAM ►https://goo.gl/SNmyt1
This is not a important part of the description but this is the person behind the "kilObit" you may wanna know him.
Thank You! for all of your support...

What is SPEECH SYNTHESIS? What does SPEECH SYNTHESIS mean? SPEECH SYNTHESIS meaning - SPEECH SYNTHESIS definition - SPEECH SYNTHESIS explanation.
Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license.
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Speech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech computer or speech synthesizer, and can be implemented in software or hardware products. A text-to-speech (TTS) system converts normal language text into speech; other systems render symbolic linguistic representations like phonetic transcriptions into speech.
Synthesized speech can be created by concatenating pieces of recorded speech that are stored in a database. Systems differ in the size of the stored speech units; a system that stores phones or diphones provides the largest output range, but may lack clarity. For specific usage domains, the storage of entire words or sentences allows for high-quality output. Alternatively, a synthesizer can incorporate a model of the vocal tract and other human voice characteristics to create a completely "synthetic" voice output.
The quality of a speech synthesizer is judged by its similarity to the human voice and by its ability to be understood clearly. An intelligible text-to-speech program allows people with visual impairments or reading disabilities to listen to written words on a home computer. Many computer operating systems have included speech synthesizers since the early 1990s.
A text-to-speech system (or "engine") is composed of two parts: a front-end and a back-end. The front-end has two major tasks. First, it converts raw text containing symbols like numbers and abbreviations into the equivalent of written-out words. This process is often called text normalization, pre-processing, or tokenization. The front-end then assigns phonetic transcriptions to each word, and divides and marks the text into prosodic units, like phrases, clauses, and sentences. The process of assigning phonetic transcriptions to words is called text-to-phoneme or grapheme-to-phoneme conversion. Phonetic transcriptions and prosody information together make up the symbolic linguistic representation that is output by the front-end. The back-end—often referred to as the synthesizer—then converts the symbolic linguistic representation into sound. In certain systems, this part includes the computation of the target prosody (pitch contour, phoneme durations), which is then imposed on the output speech.

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How does Text To Speech (TTS) work - by Acapela Voices

Welcome to this talking presentation! You are wondering what is the process to create a sy...

How does Text To Speech (TTS) work - by Acapela Voices

Welcome to this talking presentation! You are wondering what is the process to create a synthetic voice? And how does it work? Listen to this short story featuring some of our bright voices. This talking presentation uses SlideTalk online solution and Acapela Group voices.
More on http://www.slidetalk.net and http:www.acapela-group.com

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DeepMind's WaveNet Text-To-Speech Algorithm

Today's artificial speech tends to sound robotic, but using a new system called WaveNet, G...

DeepMind's WaveNet Text-To-Speech Algorithm

Today's artificial speech tends to sound robotic, but using a new system called WaveNet, Google Deepmind has created a new system that produces much more natural human speech. While not perfect, it is 50% better than current technologies. Since it is at core a general audio processor, it can also create music.
Find out more at: http://www.33rdsquare.com/2016/09/deepmind-uses-deep-neural-networks-to.html

Synthesizing Obama: Learning Lip Sync from Audio

Synthesizing Obama: Learning Lip Sync from Audio
Supasorn Suwajanakorn, Steven M. Seitz, Ira Kemelmacher-Shlizerman
SIGGRAPH 2017
Given audio of President Barack Obama, we synthesize a high quality video of him speaking with accurate lip sync, composited into a target video clip. Trained on many hours of his weekly address footage, a recurrent neural network learns the mapping from raw audio features to mouth shapes. Given the mouth shape at each time instant, we synthesize high quality mouth texture, and composite it with proper 3D pose matching to change what he appears to be saying in a target video to match the input audio track
http://grail.cs.washington.edu/projects/AudioToObama/

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The report suggests that unless preparations are made now to prevent the malicious use of the technology, cybercrime will rapidly increase in years to come ... These might include automated hacking, speechsynthesis used to impersonate targets, finely-targeted spam emails using information scraped from social media, or exploiting the vulnerabilities of AI systems themselves the reports states ... ....

Artificial intelligence (AI) poses a range of threats to cyber, physical and political security, according to a report by 26 UK and US experts and researchers ... “Because cyber security today is largely labour-constrained, it is ripe with opportunities for automation using AI ... They also expect new attacks that exploit human vulnerabilities by using speechsynthesis for impersonation, for example ... Read more about AI and security ... ....

Samsung Bixby, the digital voice assistant from the South Korean company, will become a year old assistant next month ... Samsung first failed to make Bixby voice feature available at the time of launch, and then its roll out was hit due to lack of speechsynthesis and training data. In comparison with other assistants, Bixby is also limited in terms of language support and is only available in Korean, English and Chinese ... Also Read ... ....

Galvan’s team of PRI professionals and a handful of museum volunteers went on a floor-by-floor investigation of the museum and Custom House back in October of last year. Galvan came back to the museum on Jan ... Advertisement. Continue reading ... (Deming Headlight) ... An Ovilus is an electronic speech-synthesis device which utters words depending on electromagnetic waves in the air, by using an EMF (electromagnetic field) Meter ... ....

If only it were as simple as that ... “Do you ever look around you and think ... Cameras in her eyes and a 3D sensor in her chest help her to “see,” while the processor that serves as her brain combines facial and speech recognition, natural language processing, speechsynthesis and a motion control system. Sophia seems friendly and engaging, despite the unnatural pauses and cadence in her speech ... ....

If only it were as simple as that ... "Is it weird to be talking to a robot right now?" ... Cameras in her eyes and a 3D sensor in her chest help her to "see," while the processor that serves as her brain combines facial and speech recognition, natural language processing, speechsynthesis and a motion control system. Sophia seems friendly and engaging, despite the unnatural pauses and cadence in her speech ... "Sorry to disappoint you." ... ....

If only it were as simple as that ... Cameras in her eyes and a 3-D sensor in her chest help her to "see," while the processor that serves as her brain combines facial and speech recognition, natural language processing, speechsynthesis and a motion control system. ​Sophia's predecessors Sophia seems friendly and engaging, despite the unnatural pauses and cadence in her speech ... They variously leer, blink, smile and even crack jokes ... ....

iFLYTEK has won a series of global awards for speech recognition, speechsynthesis, machine understating and logic thinking including the CHiME Challenge champion, Blizzard Challenge and Winograd Schema Challenge 2016 ... iFLYTEK's intelligent speech and artificial intelligence technologies, such as speechsynthesis, speech recognition, speech evaluation, and natural language processing, represent the top level in the world....

Amazon’s Alexa is leading the AI assistant pack. Echo devices are dominating smart speaker sales, and that was before Amazon brought the devices to more than 80 nations around the world. To defend its crown, Amazon moved fast this year to outpace competitors like Google Assistant and Microsoft’s Cortana ... Earlier this year, SpeechSynthesisMarkup Language (SSML) tags were introduced to give Alexa a more expressive voice ... ....

The company is working on a new text-to-speech system called Tacotron 2 which is essentially a neural network architecture for speechsynthesis directly from text you see on the screen ...Majority of text-to-speech (TTS) — or speechsynthesis — systems are based on “concatenative TTS, which uses a large database of high-quality recordings, collected from a single voice actor over many hours....

Scientists have developed a drumming robot, called Mortimer, who can compose music responsively to human pianists in real-time, and also post pictures of the sessions on Facebook ... During the study, two groups of participants were chosen ... They were greeted by Mortimer, who communicates via speechsynthesis software, and used a tablet to interact with him ... ....